About: Multi-state epidemic processes on complex networks

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Multi-state epidemic processes on complex networks
Creator	Masuda, Naoki Konno, Norio
source	ArXiv
abstract	Infectious diseases are practically represented by models with multiple states and complex transition rules corresponding to, for example, birth, death, infection, recovery, disease progression, and quarantine. In addition, networks underlying infection events are often much more complex than described by meanfield equations or regular lattices. In models with simple transition rules such as the SIS and SIR models, heterogeneous contact rates are known to decrease epidemic thresholds. We analyze steady states of various multi-state disease propagation models with heterogeneous contact rates. In many models, heterogeneity simply decreases epidemic thresholds. However, in models with competing pathogens and mutation, coexistence of different pathogens for small infection rates requires network-independent conditions in addition to heterogeneity in contact rates. Furthermore, models without spontaneous neighbor-independent state transitions, such as cyclically competing species, do not show heterogeneity effects.
has issue date	2005-04-13(xsd:dateTime)
bibo:doi	10.1016/j.jtbi.2006.06.010
has license	arxiv
sha1sum (hex)	f5a816cbca04dc4caa8f0f73e37ac7387d67b402
schema:url	https://doi.org/10.1016/j.jtbi.2006.06.010
resource representing a document's title	Multi-state epidemic processes on complex networks
resource representing a document's body	covid:f5a816cbca04dc4caa8f0f73e37ac7387d67b402#body_text
is schema:about of	named entity 'STEADY' named entity 'DISEASE PROGRESSION' named entity 'KNOWN' named entity 'RATES' named entity 'NETWORKS' »more»

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